Paper Citation Record · LEDGER

FBI-LLM: Scaling Up Fully Binarized LLMs from Scratch via Autoregressive Distillation

As of 7 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:2407.07093.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2407.07093 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 4 of 4 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

measured 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-02T01:35:43.528232Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-05-21T23:44:26.563485Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 9e716d84-766f-4dc6-aed0-b8be8d68a267 · inbound

BTC-LLM: Efficient Sub-1-Bit LLM Quantization via Learnable Transformation and Binary Codebook cites this paper.

BTC-LLM: Efficient Sub-1-Bit LLM Quantization via Learnable Transformation and Binary Codebook FBI-LLM: Scaling Up Fully Binarized LLMs from Scratch via Autoregressive Distillation

Reference 21

Resolution: verified exact; arxiv_id, observed 2026-05-19T14:07:20.500384Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 228f981d-3990-4ee7-9114-36f5423c8cd1 · inbound

Rethinking 1-bit Optimization Leveraging Pre-trained Large Language Models cites this paper.

Rethinking 1-bit Optimization Leveraging Pre-trained Large Language Models FBI-LLM: Scaling Up Fully Binarized LLMs from Scratch via Autoregressive Distillation

Reference 21

Resolution: verified exact; arxiv_id, observed 2026-05-21T23:44:26.565633Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation eff43df2-b394-4e4e-a658-a82b267efead · inbound

LBLLM: Lightweight Binarization of Large Language Models via Three-Stage Distillation cites this paper.

LBLLM: Lightweight Binarization of Large Language Models via Three-Stage Distillation FBI-LLM: Scaling Up Fully Binarized LLMs from Scratch via Autoregressive Distillation

Reference 34

Resolution: metadata mismatch; arxiv_id, observed 2026-05-11T12:46:04.794501Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation c74d2b19-e863-46c6-89a3-37c4cf22ceef · inbound

Cross-Layer Error Compensation and Finite-Sample Feature-Statistics Matching for Extreme Low-Bit Quantization of Large Language Models cites this paper.

Cross-Layer Error Compensation and Finite-Sample Feature-Statistics Matching for Extreme Low-Bit Quantization of Large Language Models FBI-LLM: Scaling Up Fully Binarized LLMs from Scratch via Autoregressive Distillation

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-02T01:35:43.528232Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.